

import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= VideoMAEConfig()

			set_architecture_configs(a_  ,	a_)

			if "finetuned" not in model_name:
						a_						:						List[str] 							= False

			if "finetuned" in model_name:
						a_						:						Optional[int] 							= "huggingface/label-files"
						if "kinetics" in model_name:
									a_						:						List[str] 							= 4_0_0
									a_						:						int 							= "kinetics400-id2label.json"
						elif "ssv2" in model_name:
									a_						:						int 							= 1_7_4
									a_						:						List[str] 							= "something-something-v2-id2label.json"
						else:
									raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""")
						a_						:						Union[str, Any] 							= json.load(open(hf_hub_download(a_  ,	a_  ,	repo_type="""dataset""")  ,	"""r"""))
						a_						:						List[Any] 							= {int(a_): v for k, v in idalabel.items()}
						a_						:						Any 							= idalabel
						a_						:						List[str] 							= {v: k for k, v in idalabel.items()}

			return config


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			if "small" in model_name:
						a_						:						Tuple 							= 3_8_4
						a_						:						Any 							= 1_5_3_6
						a_						:						Optional[int] 							= 1_2
						a_						:						str 							= 1_6
						a_						:						List[Any] 							= 1_2
						a_						:						Dict 							= 3
						a_						:						List[Any] 							= 1_9_2
						a_						:						Dict 							= 7_6_8
			elif "large" in model_name:
						a_						:						List[Any] 							= 1_0_2_4
						a_						:						Dict 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						List[str] 							= 1_6
						a_						:						List[Any] 							= 1_2
						a_						:						str 							= 8
						a_						:						Tuple 							= 5_1_2
						a_						:						Optional[Any] 							= 2_0_4_8
			elif "huge" in model_name:
						a_						:						List[str] 							= 1_2_8_0
						a_						:						Optional[int] 							= 5_1_2_0
						a_						:						Optional[Any] 							= 3_2
						a_						:						List[str] 							= 1_6
						a_						:						str 							= 1_2
						a_						:						Tuple 							= 8
						a_						:						Optional[Any] 							= 6_4_0
						a_						:						str 							= 2_5_6_0
			elif "base" not in model_name:
						raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""")


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if "encoder." in name:
						a_						:						Union[str, Any] 							= name.replace("""encoder."""  ,	"""""")
			if "cls_token" in name:
						a_						:						Optional[Any] 							= name.replace("""cls_token"""  ,	"""videomae.embeddings.cls_token""")
			if "decoder_pos_embed" in name:
						a_						:						int 							= name.replace("""decoder_pos_embed"""  ,	"""decoder.decoder_pos_embed""")
			if "pos_embed" in name and "decoder" not in name:
						a_						:						Optional[int] 							= name.replace("""pos_embed"""  ,	"""videomae.embeddings.position_embeddings""")
			if "patch_embed.proj" in name:
						a_						:						List[str] 							= name.replace("""patch_embed.proj"""  ,	"""videomae.embeddings.patch_embeddings.projection""")
			if "patch_embed.norm" in name:
						a_						:						Optional[Any] 							= name.replace("""patch_embed.norm"""  ,	"""videomae.embeddings.norm""")
			if "decoder.blocks" in name:
						a_						:						Optional[Any] 							= name.replace("""decoder.blocks"""  ,	"""decoder.decoder_layers""")
			if "blocks" in name:
						a_						:						Tuple 							= name.replace("""blocks"""  ,	"""videomae.encoder.layer""")
			if "attn.proj" in name:
						a_						:						Any 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "attn" in name and "bias" not in name:
						a_						:						Union[str, Any] 							= name.replace("""attn"""  ,	"""attention.self""")
			if "attn" in name:
						a_						:						Dict 							= name.replace("""attn"""  ,	"""attention.attention""")
			if "norm1" in name:
						a_						:						List[str] 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name:
						a_						:						List[Any] 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						int 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "decoder_embed" in name:
						a_						:						Union[str, Any] 							= name.replace("""decoder_embed"""  ,	"""decoder.decoder_embed""")
			if "decoder_norm" in name:
						a_						:						int 							= name.replace("""decoder_norm"""  ,	"""decoder.decoder_norm""")
			if "decoder_pred" in name:
						a_						:						Union[str, Any] 							= name.replace("""decoder_pred"""  ,	"""decoder.decoder_pred""")
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						a_						:						Any 							= name.replace("""norm.weight"""  ,	"""videomae.layernorm.weight""")
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						a_						:						Union[str, Any] 							= name.replace("""norm.bias"""  ,	"""videomae.layernorm.bias""")
			if "head" in name and "decoder" not in name:
						a_						:						int 							= name.replace("""head"""  ,	"""classifier""")

			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						a_						:						int 							= orig_state_dict.pop(a_)

						if key.startswith("""encoder."""):
									a_						:						Any 							= key.replace("""encoder."""  ,	"""""")

						if "qkv" in key:
									a_						:						str 							= key.split(""".""")
									if key.startswith("""decoder.blocks"""):
												a_						:						Any 							= config.decoder_hidden_size
												a_						:						str 							= int(key_split[2])
												a_						:						Any 							= "decoder.decoder_layers."
												if "weight" in key:
															a_						:						Optional[Any] 							= val[:dim, :]
															a_						:						List[Any] 							= val[dim : dim * 2, :]
															a_						:						Union[str, Any] 							= val[-dim:, :]
									else:
												a_						:						int 							= config.hidden_size
												a_						:						Union[str, Any] 							= int(key_split[1])
												a_						:						Dict 							= "videomae.encoder.layer."
												if "weight" in key:
															a_						:						List[Any] 							= val[:dim, :]
															a_						:						Union[str, Any] 							= val[dim : dim * 2, :]
															a_						:						Tuple 							= val[-dim:, :]
						else:
									a_						:						Dict 							= val

			return orig_state_dict


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Optional[Any] 							= hf_hub_download(
			    repo_id="""hf-internal-testing/spaghetti-video"""  ,	filename="""eating_spaghetti.npy"""  ,	repo_type="""dataset""")
			a_						:						int 							= np.load(a_)
			return list(a_)


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						str 							= get_videomae_config(a_)

			if "finetuned" in model_name:
						a_						:						Union[str, Any] 							= VideoMAEForVideoClassification(a_)
			else:
						a_						:						Optional[int] 							= VideoMAEForPreTraining(a_)

			# download original checkpoint, hosted on Google Drive
			a_						:						Optional[int] 							= "pytorch_model.bin"
			gdown.cached_download(a_  ,	a_  ,	quiet=a_)
			a_						:						int 							= torch.load(a_  ,	map_location="""cpu""")
			if "model" in files:
						a_						:						Dict 							= files["model"]
			else:
						a_						:						int 							= files["module"]
			a_						:						Dict 							= convert_state_dict(a_  ,	a_)

			model.load_state_dict(a_)
			model.eval()

			# verify model on basic input
			a_						:						int 							= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,	image_std=[0.5, 0.5, 0.5])
			a_						:						Union[str, Any] 							= prepare_video()
			a_						:						Tuple 							= image_processor(a_  ,	return_tensors="""pt""")

			if "finetuned" not in model_name:
						a_						:						List[Any] 							= hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""  ,	filename="""bool_masked_pos.pt""")
						a_						:						List[str] 							= torch.load(a_)

			a_						:						str 							= model(**a_)
			a_						:						Dict 							= outputs.logits

			a_						:						str 							= [
			    "videomae-small-finetuned-kinetics",
			    "videomae-small-finetuned-ssv2",
			    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
			    "videomae-base-short",
			    "videomae-base-short-finetuned-kinetics",
			    "videomae-base",
			    "videomae-base-finetuned-kinetics",
			    "videomae-large",
			    "videomae-large-finetuned-kinetics",
			    "videomae-huge-finetuned-kinetics",
			    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
			    "videomae-base-short-ssv2",
			    "videomae-base-short-finetuned-ssv2",
			    "videomae-base-ssv2",
			    "videomae-base-finetuned-ssv2",
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "videomae-small-finetuned-kinetics":
						a_						:						List[str] 							= torch.Size([1, 4_0_0])
						a_						:						List[Any] 							= torch.tensor([-0.9291, -0.4061, -0.9307])
			elif model_name == "videomae-small-finetuned-ssv2":
						a_						:						Tuple 							= torch.Size([1, 1_7_4])
						a_						:						int 							= torch.tensor([0.2671, -0.4689, -0.8235])
			elif model_name == "videomae-base":
						a_						:						str 							= torch.Size([1, 1_4_0_8, 1_5_3_6])
						a_						:						Union[str, Any] 							= torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]])
			elif model_name == "videomae-base-short":
						a_						:						Dict 							= torch.Size([1, 1_4_0_8, 1_5_3_6])
						a_						:						Tuple 							= torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]])
						# we verified the loss both for normalized and unnormalized targets for this one
						a_						:						Optional[Any] 							= torch.tensor([0.5142]) if config.norm_pix_loss else torch.tensor([0.6469])
			elif model_name == "videomae-large":
						a_						:						Any 							= torch.Size([1, 1_4_0_8, 1_5_3_6])
						a_						:						List[Any] 							= torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]])
			elif model_name == "videomae-large-finetuned-kinetics":
						a_						:						int 							= torch.Size([1, 4_0_0])
						a_						:						str 							= torch.tensor([0.0771, 0.0011, -0.3625])
			elif model_name == "videomae-huge-finetuned-kinetics":
						a_						:						Optional[int] 							= torch.Size([1, 4_0_0])
						a_						:						Optional[int] 							= torch.tensor([0.2433, 0.1632, -0.4894])
			elif model_name == "videomae-base-short-finetuned-kinetics":
						a_						:						Optional[int] 							= torch.Size([1, 4_0_0])
						a_						:						Tuple 							= torch.tensor([0.6588, 0.0990, -0.2493])
			elif model_name == "videomae-base-finetuned-kinetics":
						a_						:						Union[str, Any] 							= torch.Size([1, 4_0_0])
						a_						:						List[Any] 							= torch.tensor([0.3669, -0.0688, -0.2421])
			elif model_name == "videomae-base-short-ssv2":
						a_						:						Union[str, Any] 							= torch.Size([1, 1_4_0_8, 1_5_3_6])
						a_						:						str 							= torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]])
			elif model_name == "videomae-base-short-finetuned-ssv2":
						a_						:						Optional[Any] 							= torch.Size([1, 1_7_4])
						a_						:						List[str] 							= torch.tensor([-0.0537, -0.1539, -0.3266])
			elif model_name == "videomae-base-ssv2":
						a_						:						List[str] 							= torch.Size([1, 1_4_0_8, 1_5_3_6])
						a_						:						Any 							= torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]])
			elif model_name == "videomae-base-finetuned-ssv2":
						a_						:						int 							= torch.Size([1, 1_7_4])
						a_						:						List[str] 							= torch.tensor([0.1961, -0.8337, -0.6389])
			else:
						raise ValueError(f'''Model name not supported. Should be one of {model_names}''')

			# verify logits
			assert logits.shape == expected_shape
			if "finetuned" in model_name:
						assert torch.allclose(logits[0, :3]  ,	a_  ,	atol=1e-4)
			else:
						print("""Logits:"""  ,	logits[0, :3, :3])
						assert torch.allclose(logits[0, :3, :3]  ,	a_  ,	atol=1e-4)
			print("""Logits ok!""")

			# verify loss, if applicable
			if model_name == "videomae-base-short":
						a_						:						Any 							= outputs.loss
						assert torch.allclose(a_  ,	a_  ,	atol=1e-4)
						print("""Loss ok!""")

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a_)
						model.save_pretrained(a_)

			if push_to_hub:
						print("""Pushing to the hub...""")
						model.push_to_hub(a_  ,	organization="""nielsr""")


if __name__ == "__main__":
	__snake_case				:      Any   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
	    type=str,
	    help=(
	        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
	        """ download link."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	__snake_case				:      Dict   		=			parser.parse_args()
	convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



